Final Words

At a high level, the Titan supercomputer delivers an order of magnitude increase in performance over the outgoing Jaguar system at roughly the same energy price. Using over 200,000 AMD Opteron cores, Jaguar could deliver roughly 2.3 petaflops of performance at around 7MW of power consumption. Titan approaches 300,000 AMD Opteron cores but adds nearly 19,000 NVIDIA K20 GPUs, delivering over 20 petaflops of performance at "only" 9MW. The question remains: how can it be done again?

In 4 years, Titan will be obsolete and another set of upgrades will have to happen to increase performance in the same power envelope. By 2016 ORNL hopes to be able to build a supercomputer capable of 10x the performance of Titan but within a similar power envelope. The trick is, you don't get the performance efficiency from first adopting GPUs for compute a second time. ORNL will have to rely on process node shrinks and improvements in architectural efficiency, on both CPU and GPU fronts, to deliver the next 10x performance increase. Over the next few years we'll see more integration between the CPU and GPU with an on-die communication fabric. The march towards integration will help improve usable performance in supercomputers just as it will in client machines.

Increasing performance by 10x in 4 years doesn't seem so far fetched, but breaking the 1 Exaflop barrier by 2020 - 2022 will require something much more exotic. One possibility is to move from big beefy x86 CPU cores to billions of simpler cores. Given ORNL's close relationship with NVIDIA, it's likely that the smartphone core approach is being advocated internally. Everyone involved has differing definitions of what is a simple core (by 2020 Haswell will look pretty darn simple), but it's clear that whatever comes after Titan's replacement won't just look like a bigger, faster Titan. There will have to be more fundamental shifts in order to increase performance by 2 orders of magnitude over the next decade. Luckily there are many research projects that have yet to come to fruition. Die stacking and silicon photonics both come to mind, even though we'll need more than just that.

It's incredible to think that the most recent increase in supercomputer performance has its roots in PC gaming. These multi-billion transistor GPUs first came about to improve performance and visual fidelity in 3D games. The first consumer GPUs were built to better simulate reality so we could have more realistic games. It's not too surprising then to think that in the research space the same demands apply, although in pursuit of a different goal: to create realistic models of the world and universe around us. It's honestly one of the best uses of compute that I've ever seen.

For your second question, if it has the right software then any high-end consumer desktop PC could become self-aware. It would work rather sluggishly, compared to some sci-fi AIs like those in the Halo universe, but would potentially start learning and teaching itself.Reply

Hethos that is not by any stretch certain. Since "self awareness" or "consciousness" has never been engineered or simulated, it is still quite uncertain what the specific requirements would be to produce it. Yet here you're not only postulating that all it would take would be the right OS but also how well it would perform. My guess is that Titan would much sooner be able to simulate a brain (and therefore be able to learn, think, dream, and do all the things that brains do) much sooner than it would /become/ "a brain" It look a 128 core computer a 10hr run render a few-minute simulation of a complete single celled organism . Hard to say how much more compute power it would take to fully simulate a brain and be able to interact with it in real time. as for other methods of AI, it may take totally different kinds of hardware and networking all together. Reply

In addition to the bit about ECC, nVidia really made headway over AMD primarily because of CUDA. nVidia specially targeted a whole bunch of developers of popular academic software and loaned out free engineers. Experienced devs from nVidia would actually do most of the legwork to port MPI code to CUDA, while AMD did nothing of the sort. Therefore, there is now a large body of well-optimized computational simulation software that supports CUDA (and not OpenCL). However, this is slowly changing and OpenCL is catching on. Reply

I was actually surprise at how many actual times the word "actually" was actually used. Actually, the way it's actually used in this actual article it's actually meaningless and can actually be dropped, actually, most of the actual time.Reply